Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates.
Symmetry Teleportation for Accelerated Optimization
Authors: Bo Zhao, Nima Dehmamy, Robin Walters, Rose Yu
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 16,101 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,349 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we show that teleportation improves the convergence speed of gradient descent and Ada Grad for several optimization problems including test functions, multi-layer regressions, and MNIST classification. |
| Researcher Affiliation | Collaboration | Bo Zhao University of California, San Diego EMAIL Nima Dehmamy IBM Research EMAIL Robin Walters Northeastern University EMAIL Rose Yu University of California, San Diego EMAIL |
| Pseudocode | Yes | Algorithm 1: Symmetry Teleportation |
| Open Source Code | Yes | Our code is available at https://github.com/Rose-STL-Lab/Symmetry-Teleportation. |
| Open Datasets | Yes | MNIST classification. We apply symmetry teleportation on the MNIST classification task (Deng, 2012). |
| Dataset Splits | Yes | We split the training set into 48,000 for training and 12,000 for validation. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used (e.g., GPU/CPU models, memory) to run the experiments in the main text. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies or libraries used in the experiments. |
| Experiment Setup | Yes | Rosenbrock function: "Each algorithm is run 1000 steps with learning rate 10 3. We teleport the parameters every 100 steps." MNIST classification: "Learning rate is 2 10 3, and learning rate for teleportation is 10 3. Each optimization algorithm is run 80 epochs with batch size of 20." |